Numerous face and body part detection methods have been proposed in prior art, including the use of face template matching, deformable template matching or neural network classification. Some prior art body part detection and tracking depend on an analysis involving a comparison between the possible face or body part and some pre-derived data indicative of the presence of either a face or a body part.
Such methods may not be able to distinguish an image region which, while possibly looking nothing like a face or a body part, may possess certain image attributes that may enable it to pass the comparison test. Such a region may then be assigned a high probability of containing a face or a body part and can lead to a false-positive. It is a constant aim in this technical field to improve the reliability of face detection, including reducing the occurrence of false-positive candidates.
Additionally, the processing required to recognize and track a body part tends to be resource intensive, consuming significant amounts of CPU cycles and draining the battery on a mobile/portable device.
It would be desirable to address at least some of these limitations of the prior art by providing an efficient method and a system for body part detection and tracking that could be used for example on mobile devices.